It takes years – sometimes a lifetime – to perfect certain skills in life: hitting a jump shot off the dribble, nailing that double high C on the trumpet, parallel parking a Ford Expedition. Malcolm Gladwell wrote a book, “Outliers,” discussing the amount of work – 10,000 hours – required to perfect a skill (while the exactness of 10,000 hours has come under debate, it is still a useful point that people need to invest considerable time and effort to master a skill). But once we get comfortable with something that we feel that we have mastered, we become reluctant to change. We are reluctant to unlearn what we’ve taken so long to master.

Changing your point of release on a jump shot or your embouchure for playing lead trumpet is dang hard! Why? Because it is harder to unlearn that it is to learn. It is harder to un-wire all those synoptic nerve endings and deep memories than it was to wire them in the first place. It’s not just a case of thinking faster, smaller or cheaper; it necessitates thinking differently.

For example, why did it take professional basketball so long to understand the game changing potential of the 3-point shot? The 3-point shot was added to the NBA during the 1979-1980 season, but for decades the 3-point shot was more a novelty then a serious game strategy. Pat Riley, the legendary coach of the 3-pointer’s first decade in the league (won NBA Championships in 1982, 1985, 1987 and 1988), called it a “gimmick.” Larry Bird, one of that era’s top players said: “I really don’t like it.”

It’s only been within the past 3 years where the “economics of the 3-point shot” have changed the fundamentals of how to win an NBA Championship (see Figure 1).

NBA Coaches and General Managers just didn’t comprehend the “economics of the 3-point shot” and how the 3-point shot could turn a good shooter into a dominant player; that a 40% 3-point shooting percentage is equivalent to a 60% 2-point shooting percentage from a points / productivity perspective. The economics of the 3-point shot (coupled with rapid ball movement to create uncontested 3-point shots) wasn’t full exploited until the 2015-2016 season by the Golden State Warriors. Their success over the past 3 seasons (3 trips to the NBA finals with 2 championships) shows how much the game of basketball has been changed.

Sometimes it’s necessary to unlearn long held beliefs (i.e. 2-point shooting in a predominately isolation offense game) in order to learn new, more powerful, game changing beliefs (i.e., 3-point shooting in a rapid ball movement offense).

Sticking with our NBA example, Phil Jackson is considered one of the greatest NBA coaches, with 11 NBA World Championships coaching the Chicago Bulls and the Los Angeles Lakers. Phil Jackson mastered the “Triangle Offense” that played to the strengths of the then dominant players Michael Jordan (Chicago Bulls) and Kobe Bryant (Los Angeles Lakers) to win those 11 titles.

However, the game passed Phil Jackson as the economics of the 3-point shot changed how to win. Jackson’s tried-and-true “Triangle Offense” failed with the New York Knicks leading to the team’s dramatic under-performance and ultimately his firing. It serves as a stark reminder of how important it is to be ready to unlearn old skills in order to move forward.

And what holds true for sports, holds even more so for technology and business.

The Challenge of UnlearningFor the first two decades of my career, I worked to perfect the art of data warehousing. I was fortunate to be at Metaphor Computers in the 1980’s where we refined the art of dimensional modeling and star schemas. I had many years working to perfect my star schema and dimensional modeling skills with data warehouse luminaries like Ralph Kimball, Margy Ross, Warren Thornthwaite, and Bob Becker. It became engrained in every customer conversation; I’d built a star schema and the conformed dimensions in my head as the client explained their data analysis requirements.

Then Yahoo happened to me and soon everything that I held as absolute truth was turned upside down. I was thrown into a brave new world of analytics based upon petabytes of semi-structured and unstructured data, hundreds of millions of customers with 70 to 80 dimensions and hundreds of metrics, and the need to make campaign decisions in fractions of a second. There was no way that my batch “slice and dice” business intelligence and highly structured data warehouse approach was going to work in this brave new world of real-time, predictive and prescriptive analytics.

I struggled to unlearn engrained data warehousing concepts in order to embrace this new real-time, predictive and prescriptive world. And this is one of the biggest challenge facing IT leaders today – how to unlearn what they’ve held as gospel and embrace what is new and different. And nowhere do I see that challenge more evident then when I’m discussing Data Science and the Data Lake.

Embracing The “Art of Failure” and The Data Science ProcessNowadays, Chief Information Officers (CIOs) are being asked to lead the digital transformation from a batch world that uses data and analytics to monitor the business to a real-time world that exploits internal and external, structured and unstructured data, to predict what is likely to happen and prescribe recommendations. To power this transition, CIO’s must embrace a new approach for deriving customer, product, and operational insights – the Data Science Process (see Figure 2).

Figure 2: Data Science Engagement Process

The Data Science Process is about exploring, experimenting, and testing new data sources and analytic tools quickly, failing fast but learning faster. The Data Science process requires business leaders to get comfortable with “good enough” and failing enough times before one becomes comfortable with the analytic results. Predictions are not a perfect world with 100% accuracy. As Yogi Berra famously stated:

“It’s tough to make predictions, especially about the future.”

This highly iterative, fail-fast-but-learn-faster process is the heart of digital transformation – to uncover new customer, product, and operational insights that can optimize key business and operational processes, mitigate regulatory and compliance risks, uncover new revenue streams and create a more compelling, more prescriptive customer engagement. And the platform that is enabling digital transformation is the Data Lake.

The Power of the Data LakeThe data lake exploits the “economics of big data”; coupling commodity, low-cost servers and storage with open source tools and technologies, is 50x to 100x cheaper to store, manage and analyze data then using traditional, proprietary data warehousing technologies. However, it’s not just cost that makes the data lake a more compelling platform than the data warehouse. The data lake also provides a new way to power the business, based upon new data and analytics capabilities, agility, speed, and flexibility (see Table 1).

Generates predictions and prescriptions from a wide variety of internal and external data sources

100% accurate results of past events and performance

“Good enough” predictions of future events and performance

Schema-on-load to support the historical reporting on what the business did

Schema-on-query to support the rapid data exploration and hypothesis testing

Extremely difficult to ingest and explore new data sources (measured in weeks or months)

Easy and fast to ingest and explore new data sources (measured in hours or days)

Monolithic design and implementation (water fall)

Natively parallel scale out design and implementation (scrum)

Expensive and proprietary

Cheap and open source

Widespread data proliferation (data warehouses and data marts)

Single managed source of organizational data

Rigid; hard to change

Agile; relatively ease to change

Table 1: Data Warehouse versus Data Lake

The data lake supports the unique requirements of the data science team to:

Rapidly explore and vet new structured and unstructured data sources

Experiment with new analytics algorithms and techniques

Quantify cause and effect

Measure goodness of fit

The data science team needs to be able perform this cycle in hours or days, not weeks or months. The data warehouse cannot support these data science requirements. The data warehouse cannot rapidly exploration the internal and external structured and unstructured data sources. The data warehouse cannot leverage the growing field of deep learning/machine learning/artificial intelligence tools to quantify cause-and-effect. Thinking that the data lake is “cold storage for our data warehouse” – as one data warehouse expert told me – misses the bigger opportunity. That’s yesterday’s “triangle offense” thinking. The world has changed, and just like how the game of basketball is being changed by the “economics of the 3-point shot,” business models are being changed by the “economics of big data.”

But a data lake is more than just a technology stack. To truly exploit the economic potential of the organization’s data, the data lake must come with data management services covering data accuracy, quality, security, completeness and governance. See “Data Lake Plumbers: Operationalizing the Data Lake” for more details (see Figure 3).

Figure 3: Components of a Data Lake

If the data lake is only going to be used another data repository, then go ahead and toss your data into your unmanageable gaggle of data warehouses and data marts.

BUT if you are looking to exploit the unique characteristics of data and analytics –assets that never deplete, never wear out and can be used across an infinite number of use cases at zero marginal cost – then the data lake is your “collaborative value creation” platform. The data lake becomes that platform that supports the capture, refinement, protection and re-use of your data and analytic assets across the organization.

But one must be ready to unlearn what they held as the gospel truth with respect to data and analytics; to be ready to throw away what they have mastered to embrace new concepts, technologies, and approaches. It’s challenging, but the economics of big data are too compelling to ignore. In the end, the transition will be enlightening and rewarding. I know, because I have made that journey.

DXWorldEXPO LLC, the producer of the world's most influential technology conferences and trade shows has announced the conference tracks for CloudEXPO|DXWorldEXPO 2018 New York.

DXWordEXPO New York 2018, colocated with CloudEXPO New York 2018will be held November 11-13, 2018, in New York City.

Digital Transformation (DX) is a major focus with the introduction of DXWorldEXPO within the program. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term.

A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throughout enterprises of all sizes.

DXWorldEXPO | CloudEXPO 2018 New Yorkcover all of these tools, with the most comprehensive program and with 222 rockstar speakers throughout our industry presenting 22 Keynotes and General Sessions, 200 Breakout Sessions along 10 Tracks, as well as our signature Power Panels. Our Expo Floor brings together the world's leading companies throughout the world of Cloud Computing, DevOps, FinTech, Digital Transformation, and all they entail.

As your enterprise creates a vision and strategy that enables you to create your own unique, long-term success, learning about all the technologies involved is essential. Companies today not only form multi-cloud and hybrid cloud architectures, but create them with built-in cognitive capabilities.

Cloud-Native thinking is now the norm in financial services, manufacturing, telco, healthcare, transportation, energy, media, entertainment, retail and other consumer industries, as well as the public sector.

CloudEXPO is the world's most influential technology event where Cloud Computing was coined over a decade ago and where technology buyers and vendors meet to experience and discuss the big picture of Digital Transformation and all of the strategies, tactics, and tools they need to realize their goals.

FinTech Is Now Part of the DXWorldEXPO | CloudEXPO Program!

Financial enterprises in New York City, London, Singapore, and other world financial capitals are embracing a new generation of smart, automated FinTech that eliminates many cumbersome, slow, and expensive intermediate processes from their businesses.

Accordingly, attendees at the upcoming 22nd CloudEXPO | DXWorldEXPONovember 11-13, 2018 in New York City will find fresh new content in two new tracks called:

FinTechEXPO

New York Blockchain Event

which will incorporate FinTech and Blockchain, as well as machine learning, artificial intelligence and deep learningin these two distinct tracks.

FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry. FinTech is a natural fit with cloud computing, as new services are quickly developed, deployed, and scaled on public, private, and hybrid clouds.

More than US$20 billion in venture capital is being invested in FinTech this year. DXWorldEXPO | CloudEXPOare pleased to bring you the latest FinTech developments as an integral part of our program.

DXWorldEXPO | CloudEXPO are accepting speaking submissions for this new track, so please visit Cloud Computing Expo for the latest information or contact us at info@dxworldexpo.com.

22nd International DXWorldEXPO | CloudEXPO, taking place November 11-13, 2018, in New York City, will feature technical sessions from a rock star conference faculty and the leading industry players in the world.

Cloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy. Meanwhile, 94% of enterprises are using some form of XaaS - software, platform, and infrastructure as a service.

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.

Every Global 2000 enterprise in the world is now integrating cloud computing in some form into its IT development and operations. Midsize and small businesses are also migrating to the cloud in increasing numbers.

Companies are each developing their unique mix of cloud technologies and services, forming multi-cloud and hybrid cloud architectures and deployments across all major industries. Cloud-driven thinking has become the norm in financial services, manufacturing, telco, healthcare, transportation, energy, media, entertainment, retail and other consumer industries, and the public sector.

Sponsorship Opportunities

DXWorldEXPO | CloudEXPO are the single show where technology buyers and vendors can meet to experience and discus cloud computing and all that it entails. Sponsors of DXWorldEXPO | CloudEXPO will benefit from unmatched branding, profile building and lead generation opportunities through:

Featured on-site presentation and ongoing on-demand webcast exposure to a captive audience of industry decision-makers.

Showcase exhibition during our new extended dedicated expo hours

Breakout Session Priority scheduling for Sponsors that have been guaranteed a 35-minute technical session

DXWorldEXPO LLC is a Lighthouse Point, Florida-based trade show company and the creator of DXWorldEXPO - Digital Transformation Conference & Expo. The company produces and presents CloudEXPO, DevOpsSummit, FinTechEXPO - Blockchain Event, the world's most influential conferences and trade shows.

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Hitachi Vantara as CTO, IoT and Analytics.

Previously, as a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.